Integrated bio-economic modelling
and environmental valuation
Marit E. Kragt
School of Agricultural and Resource Economics
University of Western Australia
Australian Environmental Policy ~
Institutional arrangements
 Most environmental policies
responsibility of States
(considerable variation across
States and Territories)
 Natural Resource Management
implemented by NRM Regions
 Guiding regional NRM planning
strategies and investment
priorities within each region
 Providing mechanisms for greater
community-based NRM
Australian Environmental Policies
 National Landcare
Program (1989)
 National Heritage Trust
(1997)
 National Water Initiative
(2004)
 Caring for our Country
Program (2008)
Australian policy focus / objectives
Most programs aim to:
 Raise landholders’ NRM skills;
 Emphasis on broad participation of
landholders and community-based
regional bodies to develop and
implement integrated NRM plans;
 Instruments = Skills development
programs, technical support,
partnerships, public communication;
 Spreading dollars equally (sparsely)
Australian policy focus / objectives
Since 2000s:
 But increasingly more
targeted funding
through market-based
instruments, mostly
 water permit
trading, and
 biodiversity /
conservation tenders
Australian research examples
 DATA collection – biophysical research needed (eg.
salinity management, erosion reduction and prevention,
soil conditions)
 Landholders’ characteristics and adoption of BMPs
 Improving cost-effectiveness of policies – prioritisation
and design
 Integrating science and non-market values into decision
making
Some very recent developments
 June 2011 – passing of Carbon Farming Initiative Bill
Gives farmers access to voluntary carbon markets;
Carbon sequestration will be credited as abatement
under the National Carbon Offset Standard;
 July 2011 – Announcement of $23 Carbon Price to be
implemented in June 2012
A ‘no-tax’ to be paid by 500 largest CO2 emitters on
emissions (not capped);
Accompanied by substantial tax reform and investments
in R&D (~200 million), Biodiversity fund (~1 billion),
Carbon Farming Fund (~450 million), …..
My research focus =
integrated bio-economic modelling
1) Integrating biophysical models and nonmarket valuation
2) The costs of soil carbon
sequestration in agriculture
Integrated bio-economic modelling
Socio-economic
modelling
(costs and benefits)
Biophysical modelling
(hydrology, ecology,
agronomy)
 Complex problems and
targeted NRM requires
integrated
management,
but....
 Few decision support
tools integrate
biophysical modelling
and economic costbenefit analysis
Topic 1: Integrating biophysical models and
non-market valuation
Developing an integrated decision support model
to assess environmental and economic impacts
of catchment management changes
in Tasmania, Australia
Integrated Assessment process
1. Understanding
the issue
2. Identify NRM
changes
3. Predict biophysical
impacts
4. Predict economic
impacts
5. Evaluation
framework
1.
Describe system variables and identify issues of
concern (incl. stakeholder consultation)
2.
Describe possible NRM actions
(incl. stakeholder consultation)
3.
Predict biophysical impacts
(water quality and ecological modelling)
4.
Predict economic impacts
(management costs and non-market benefits)
5.
Evaluate trade-offs
(multiple indicators)
Case-study
 George River catchment in Tasmania
1 – Defining the issue
Consensus between natural scientists,
modellers and economists:
1. Which management changes?
Local NRM actions (weed mgt;
riparian revegetation; erosion control)
2. Which processes?
Model parsimony
3. Which variables?
Chemical and ecological indicators
Attributes relevant to policy makers
and survey respondents
1 – Conceptual modelling
2 – Conceptual modelling
Management costs
Management
actions
Non-market values
Biophysical
processes
Ecosystem
indicators
Integrated modelling challenges
 Not enough information to
develop full model
 Final model for one
attribute (riparian
vegetation)
 Demonstration of
integration approach
using Bayesian
Network techniques
17 / 21
Bayesian Networks
 Probabilistic models that define relationships between
variables by probability distributions
 Probability distributions reflect uncertainty in predictions
60
Probability
50
40
30
20
10
0
<45km
45-67km
67-78km
Outcome
>78km
18 / 21
3 – Biophysical modelling
 Develop a WQ model = George-CatchMODS
 Process-based, spatially explicit model to predict erosion,
sedimentation and nutrient loading
3 – Biophysical modelling
 Decision support tool in its own right;
 Used Monte Carlo simulations of model to characterise uncertainty;
3 – Biophysical modelling

Not enough scientific knowledge to develop ecological models

Literature, expert consultation, observations and WQ modelling

Predict highest and lowest level of attributes under various management
scenarios + (un) certainty in predictions
4 – Economic modelling
 Use Choice Experiments to assess non-market value impacts of ecosystem
changes in the George catchment
 Survey method where people choose between options with different levels of
environmental attributes and management cost
 Integration of ecological models and attribute levels
4 – Economic modelling
Costs
Environmental attributes
Trade-offs
4 – Economic modelling
 Survey administered in Tasmania
 Econometric modelling to
estimate marginal values of
changes in riparian vegetation
WTP = 3.57 $ / km
 Uncertainty indicated by
confidence interval =
2.52 to 4.61 $ / km
60
Likelihood
50
5 - Integration
40
30
20
10
0
Less 40
40-60
60-70
BN states
More 70
Monte Carlo simulations
of CatchMODS WQ model
CE value estimates
and confidence
intervals
Probabilistic modelling using assumptions
and expert interviews
Integration challenges
1. Agreement on ‘relevant’ variables / suitable indicators (especially
model parsimony);
2. Limited data to develop a full decision support system
(knowledge lacking about environmental processes, costs, and benefits);
3. Probabilistic predictions and use of expert opinions;
4. Difference in predictions:
 Biophysical modelling of levels (environmental condition)
 Economic predictions of marginal values (changes in environmental
status)
My model ‘solution’ = Use two model runs to predict environmental
levels before and after a management change;
Predict the marginal value changes associated with implementing
new management actions;
60
Likelihood
50
40
30
20
10
0
Less 40
40-60
60-70
BN states
More 70
Situation
before
Marginal
costs
Marginal
benefits
Situation
after
Example scenario:
- no stream-bank
works
- current
land use
Implementing:
- 6-12 km riparian
buffer
- high weed
management
Example scenario:
- no stream-bank works
- Current land use
Implementing:
- 6-12 km riparian buffer
- high weed
management
Example scenario:
- no stream-bank works
- Current land use
Implementing:
- 6-12 km riparian buffer
- high weed
management
Example scenario:
- no stream-bank works
- Current land use
Implementing:
- 6-12 km riparian buffer
- high weed
management
End – topic 1
Topic 2: Costs of soil carbon sequestration in
agriculture
 Agricultural soils have a
potential to store carbon
and offset CO2 emissions;
 Potential changes in
management practices:
 Tree planting;
 Conservation tillage;
 Grazing and livestock
management;
 Improved fertiliser
management;
 Increased residue retention;
 Conversion from annual to
perennial crops or pasture;
 …..
Why focus on soil carbon?
“ The single largest opportunity for CO2 emissions reduction in
Australia is through bio-sequestration in general, and in particular,
the replenishment of our soil carbons. It is also the lowest cost CO2
emissions reduction available in Australia on a large scale”
“Carbon can be captured in soils with a payment to farmers of
approximately $10 per tonne.”
Questions
How realistic is it to expect
Australian farmers
to store carbon in soils?
1. What sequestration rates
can be achieved? –
biophysical research
2. What impact would there
be on farm profits, if a
farmers chooses to
change practices? –
economic research
Method = Bio-economic model
 Case study of representative mixed farming
system in Central Wheat-belt (WA);
1) Biophysical model  Agricultural Production
Systems Simulator (APSIM)  carbon sequestration
potential
(APSIM accounts for climate, soil conditions, crop-pasture rotations,
stubble management, fertiliser use et.)
2) Whole-farm economic model  Model of Integrated
Dryland Agricultural System (MIDAS) to predict farm profit
(MIDAS is a steady-state optimisation model, accounts for soil types,
enterprise activities, input and output prices etc.)
Method = Bio-economic model
 Scenarios:
 Shifts in enterprise mix
(pasture–crop rotations);
 Increased stubble retention;
 Several commodity price
scenarios;
 Output:
 Profit-maximising combination of rotations on each soil type;
 Carbon sequestration rates under different enterprise mixes,
for different soil depths and time frames
Trade-offs profit-carbon by rotations
maximum
C-storage
maximum
profit
Trade-offs profit-carbon by rotations
maximum
C-storage
maximum
profit
Trade-offs profit-carbon by rotations
maximum
C-storage
Compensation needed to
stimulate farmers to move
from profit-maximum to
increase C-sequestration.
maximum
profit
Trade-offs by stubble retention
C-storage
increases
when moving
to full retention
Smaller
C-storing
potential
High C-storage
potential
Relatively larger profit loss
 more compensation to
increase C-sequestration on
farms that already adopt
conservation practices.
Trade-offs by stubble retention
 Increasing residue retention will increase soil organic
carbon  focus policy initiatives?
 But, (!) in the Australian Carbon Farming Initiative,
practices that are commonly used and cost-effective
without offsets are not eligible to sell for credits (not
considered ‘additional’);

Therefore  focus on
crop-pasture mix;
Opportunity costs of soil-C farming?
 We showed that changing crop rotations
to increase soil carbon will reduce farm profit;
 How much compensation would a farmer need per tonne
of CO2?
Results – Payments?
Compensation for C-storage in top-soil (averaged over 30yrs)
Annual payment ($ t-1 CO2-e)
600
500
More substantial
sequestration?
400
300
‘Low-hanging fruits’ ~
profit loss between
$40-60/t CO2-e yr
200
100
0
0
10
20
30
40
Additional soil-C stored* (kg ha-1 yr-1)
base-prices - 50% retained
Notes: Storage calculated for 0-30cm topsoil 30yr-average; 1 tonne C = 3.667 tonnes CO 2
50
Results – Payments?
Compensation for C-storage in top-soil (averaged over 30yrs)
Annual payment ($ t-1 CO2-e)
600
‘Expensive’ if under
full stubble
retention
500
400
300
200
100
0
0
10
20
30
40
Additional soil-C stored* (kg ha-1 yr-1)
base-prices - 50% retained
100% stubble retention
Notes: Storage calculated for 0-30cm topsoil 30yr-average; 1 tonne C = 3.667 tonnes CO 2
50
Discussion
 Getting farmers to change their enterprise mix to
increase soil carbon sequestration will come at significant
costs;
 Permanence obligations  risks for farmers and loss in
option values
 Additionality  farmers who already follow conservation
practices will not be eligible for compensation